WO 2011/027298 A1 discloses a projection values processing apparatus for processing acquired projection values, wherein the projection values processing apparatus comprises an acquired projection values providing unit for providing acquired projection values, a reconstruction unit for reconstructing a first image from the acquired projection values under consideration of a reconstruction assumption, and a simulated projection values determining unit for determining simulated projection values by simulating a projection through the reconstructed first image under consideration of the reconstruction assumption. The projection values processing apparatus further comprises an inconsistency determining unit for determining inconsistency values for the acquired projection values, wherein an inconsistency value is indicative of a degree of inconsistency of a respective acquired projection value with the reconstruction assumption, by comparing the acquired projection values and the simulated projection values.
The article “Robust Model-Based 3D/3D Fusion Using Sparse Matching for Minimally Invasive Surgery” by D. Neumann et al., MICCAI 2013, Part I, LNCS 8149, pages 171 to 178 (2013) discloses a sparse matching approach for fusing a high quality pre-operative computed tomography (CT) image and a non-contrasted, non-gated intra-operative C-arm CT image by utilizing robust machine learning and numerical optimization techniques.
The article “Model-Based Tomographic Reconstruction of Objects Containing Known Components” by J. Webster et al., IEEE Transactions on Medical Imaging, volume 31, number 10, pages 1837 to 1848 (2012) discloses integrating physical models of manufactured components into CT reconstruction algorithms, in order to reduce artifacts in CT images. In particular, a model-based penalized-likelihood estimation reconstruction algorithm is used, which explicitly incorporates known information about the geometry and composition of the respective manufactured component.
The article “Motion Compensated Backprojection versus Backproject-then-Warp for Motion Compensated Reconstruction” by B. Brendel et al., The Third International Conference on Image Formation in X-Ray Computed Tomography, pages 169 to 172, Salt Lake City, USA (2014) discloses a CT system for generating an image of a fast moving organ like the heart. The CT system is adapted to firstly estimate the motion of the fast moving organ and to secondly reconstruct an image of the fast moving organ using the estimated motion. For estimating the motion the CT system reconstructs images of the fast moving organ without motion compensation for different times and registers these images.
The quality of the registration of the images, which have been reconstructed without motion compensation for different times, can be reduced due to image artifacts, wherein this reduced registration quality can lead to a reduced quality of the motion estimation. Reconstructing an image of the fast moving organ based on this motion estimation can finally lead to a reconstructed image having significant motion artifacts.